A One-Nearest-Neighbor Approach to Identify the Original Time of Infection Using Censored Baboon Sepsis Data.
A One-Nearest-Neighbor Approach to Identify the Original Time of Infection Using Censored Baboon Sepsis Data.
复制标题
一种使用截尾狒狒脓毒症数据识别原始感染时间的最近邻方法。
DOI：
10.1097/ccm.0000000000001623
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发表时间：
2016
影响因子：
8.8
通讯作者：
Clermont,Gilles
中科院分区：
文献类型：
--
作者：
Zhang,LiAng;Parker,RobertS;Swigon,David;Banerjee,Ipsita;Bahrami,Soheyl;Redl,Heinz;Clermont,Gilles
目的：脓毒症治疗已被证明是难以捉摸的，因为难以将动物模型中生物学上合理和有效的干预转化为人类。这个问题的一部分源于这样的事实，即脓毒症患者在脓毒症发作后的不同时间出现，而感染的确切时间在动物模型中得到控制。我们试图确定是否数据挖掘纵向生理数据在非人灵长类动物模型的大肠杆菌引起的脓毒症可以帮助通知infection.Design的发病时间：最近邻的方法被用来倒推感染的动物模型的发病时间脓毒症。对动物数据进行删失，以模拟脓毒性感染沿着的任何时刻的前瞻性监测。将其与未删失数据库进行比较，以找到最相似的动物，从而估计感染发作时间。采用留一交叉验证法进行验证。生物标志物的选择进行了估计的准确性和/或易于measurement.Setting的标准的基础上：计算实验现有的实验data.Subjects：回顾性数据从33败血症狒狒（巴布亚熊）进行大肠杆菌输液。使用14只猪进行手术诱导的粪便腹膜炎和22只猪进行脂多糖infrations. Measures和主要结果：纵向生理和血清标志物，死亡时间进行验证。脓毒性感染期间独特变化的生物标志物的存在使得能够估计数据集中的感染发作时间。时间生物标志物的各种组合，如WBC、氧含量、平均动脉压和心率，产生了高达97.8%的估计准确度。使用时间生命体征和血清生物标志物的单一测量产生了高度准确的估计，而不需要侵入性测量。尽管多个实验队列存在异质性，但猪数据的验证显示了相似的结果。这表明，该方法可能是有效的，如果足够相似的主题是存在于database.Conclusions：最近邻分析显示承诺，在准确地确定感染的发病时间，已知的感染时间和足够的广度的数据库。我们认为，这种方法是准备在临床环境中使用人体数据进行评估。
Objectives:Sepsis therapies have proven to be elusive because of the difficulty of translating biologically sound and effective interventions in animal models to humans. A part of this problem originates from the fact that septic patients present at various times after the onset of sepsis, whereas the exact time of infection is controlled in animal models. We sought to determine whether data mining longitudinal physiologic data in a nonhuman primate model of Escherichia coli–induced sepsis could help inform the time of onset of infection.Design:A nearest-neighbor approach was used to back cast the time of onset of infection in animal models of sepsis. Animal data were censored to simulate prospective monitoring at any moment along the septic infection. This was compared against an uncensored database to find the most similar animal in order to estimate the infection onset time. Leave-one-out cross-validation was used for validation. Biomarker selection was performed based on the criteria of estimation accuracy and/or ease of measurement.Setting:Computational experimental on existing experimental data.Subjects:Retrospective data from 33 septic baboons (Papio ursinus) subjected to Escherichia coli infusion. Validation was performed using 14 pigs that were subjected to surgically induced fecal peritonitis and 22 pigs that were subjected to lipopolysaccharide infusion.Measurements and Main Results:Longitudinal physiologic and serum markers, time of death. The presence of uniquely changing biomarkers during septic infection enabled the estimation of infection onset time in the datasets. Various combinations of temporal biomarkers, such as WBC, oxygen content, mean arterial pressure, and heart rate, yielded estimation accuracies of up to 97.8%. The use of temporal vital signs and a single measurement of serum biomarkers yielded highly accurate estimates without the need for invasive measurements. Validation in the pig data revealed similar results despite the heterogeneity of multiple experimental cohorts. This suggests that the method may be effective if sufficiently similar subjects are present in the database.Conclusions:One nearest-neighbor analysis showed promise in accurately identifying the onset of infection given a database of known infection times and of sufficient breadth. We suggest that this approach is ready for evaluation within the clinical setting using human data.